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International Conference on Innovative Technologies and Learning

ICITL 2021: Innovative Technologies and Learning pp 49–58Cite as

Perusall’s Machine Learning Towards Self-regulated Learning

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 13117)

Abstract

This current work presents exploratory research related to Perusall activity. One of the objectives of this study was to analyze the Perusalll's features, with emphasis on peer work, which can increase individual motivation facilitating self-regulation learning. Perusall is a social web tool that uses a machine learning algorithm, which assesses the quality of annotations and students’ engagement. This tool was integrated with the LMS of Universidade Aberta (Portugal) and it was used as a pilot project in a Curricular Unit, from the 2nd year of the Education undergraduate program. We designed a collaborative activity inspired by Inquiry-based Learning and peer-instruction, to be performed on Perusall. 115 students, from 2 classes, were involved. To assess students’ work, their engagement and motivation (basis for self-regulation) we analyzed Perusall´s reports and scoring based on 6 different components. We also asked students to report positive and negative aspects related to their experience with Perusall. Our findings confirm that collaborative reading tools can help students to get more involved in self-learning, as well machine learning can help instructors work, namely monitoring and assessment tasks.

Keywords

  • Machine learning
  • Perusall
  • Collaborative learning
  • Self-regulated learning
  • Distance education

Financed national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the projects UIDB/04372/2020 e UIDP/04372/2020.

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References

  1. McGreal, R., Elliott, M.: Technologies of online learning (E-learning). In: T. Anderson, F. Elloumi (eds.), Theory and Practice of Online Learning (chapter 5). Athabasca University. Retrieved from http://cde.athabascau.ca/online_book/ch1.html (2004)

  2. Bates, A.W.: Technology, e-learning and Distance Education, 2nd edn. Routledge, New York (2005)

    CrossRef  Google Scholar 

  3. Garrison, D.R., Anderson, T.: E-learning in the 21st Century: A Framework for Research and Practice. Routledge, London (2003)

    CrossRef  Google Scholar 

  4. OECD: Education responses to covid-19: embracing digital learning and online collaboration. OECD (2020). https://doi.org/10.1787/d75eb0e8-en

  5. Laurillard, D.: Digital technologies and their role in achieving our ambitions for education. Institute of Education, University of London, London. Retrieved from http://eprints.ioe.ac.uk/628/1/Laurillard2008Digital_technologies.pdf (2008)

  6. Mentis, M.: Navigating the e-learning terrain: aligning technology, pedagogy and context. Electron. J. e-Learn. 6(3), 217–226. Retrieved from http://www.ejel.org/issue/download.html?idArticle=76 (2008)

  7. Maini, V., Sabri, S.: Machine Learning for Humans. Ebook (2017)

    Google Scholar 

  8. Brownlee, J.: Machine Learning Performance Improvement Cheat Sheet. [Blog]. Retrieved from https://machinelearningmastery.com/ (May 22, 2019)

  9. Jordan, M., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–326 (2015)

    CrossRef  MathSciNet  Google Scholar 

  10. Luan, H., Tsai, C.-C.: A review of using machine learning approaches for precision education. Educ. Technol. Soc. 24(1), 250–266 (2021)

    Google Scholar 

  11. Yang, A.C.M., Chen, I.Y.L., Flanagan, B., Ogata, H.: From human grading to machine grading: automatic diagnosis of e-book text marking skills in precision education. Educ. Technol. Soc. 24(1), 164–175 (2021)

    Google Scholar 

  12. Yang, S.J.H.: Precision education: New challenges for AI in education [conference keynote]. In: 27th International Conference on Computers in Education, XXVII–XXVIII. Asia-Pacific Society for Computers in Education (APSCE), Kenting, Taiwan (2019)

    Google Scholar 

  13. Pedrosa, D., Cravino, J., Morgado, L., Barreira, C.: Self-regulated Learning in Computer Programming: Strategies Students Adopted During an Assignment. In: Allison, C., Morgado, L., Pirker, J., Beck, D., Richter, J., Gütl, C. (eds.) iLRN 2016. CCIS, vol. 621, pp. 87–101. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41769-1_7

    CrossRef  Google Scholar 

  14. Zimmerman, B.J.: Self-regulated learning: theories, measures, and outcomes. In: Wright, J.D. (Ed.), International Encyclopedia of the Social & Behavioral Sciences, pp. 541–546. Elsevier, Oxford (2015). https://doi.org/10.1016/B978-0-08-097086-8.26060-1

  15. Winne, P.H., Hadwin, A.F.: Self-regulated learning and socio-cognitive theory. In: Peterson, P., Baker, E., McGaw, B. (Eds.). International Encyclopedia of Education, 3rd ed., pp. 503–508. ISBN 97.80080448947 (2010)

    Google Scholar 

  16. Ahmed, W.: Motivation and self-regulated learning: a multivariate multilevel analysis. Int. J. Psychol. Educ. Stud. 4(3), 1–11 (2017)

    CrossRef  Google Scholar 

  17. Panadero, E.: A review of self-regulated learning: six models and four directions for research. Front. Psychol. 8(422) (2017). https://doi.org/10.3389/fpsyg.2017.00422

  18. Bandura, A.: Social cognitive theory. Handbook of Social Psychological Theories, pp. 349–373 (2011)

    Google Scholar 

  19. Vygotsky, L.S.: Mind in Society: The Development of Higher Psychological Processes. Harvard University Press, Cambridge, MA (1978)

    Google Scholar 

  20. Siemens, G.: Connectivism: a learning theory for the digital age. Int. J. Instruct. Technol. Distance Learn. 2(1), 3–10 (2005)

    Google Scholar 

  21. Smith, B.L., MacGregor, J.T.: What is collaborative learning? In: Goodsell, A., Maher, M., Tinto, V., Smith, B.L., MacGregor, J.T. (Eds.), Collaborative Learning: A Sourcebook for Higher Education. Pennsylvania State University, USA: National Center on Postsecondary Teaching, Learning, and Assessment Publishing (1992)

    Google Scholar 

  22. Laal, M., Ghodsi, S.M.: Benefits of collaborative learning. Procedia Soc. Behav. Sci. 31, 486–490 (2012). https://doi.org/10.1016/j.sbspro.2011.12.091

    CrossRef  Google Scholar 

  23. Law, Q.P.S., So, H.C.F., Chung, J.W.Y.: Effect of collaborative learning on enhancement of students’ self-efficacy, social skills and knowledge towards mobile apps development. Am. J. Educ. Res. 5(1), 25–29. Retrieved from http://pubs.sciepub.com/education/5/1/4 (2017)

  24. Biasutti, M., Frate, S.: Group metacognition in online collaborative learning: validity and reliability of the group metacognition scale (GMS). Educ. Tech. Res. Dev. 66(6), 1321–1338 (2018). https://doi.org/10.1007/s11423-018-9583-0

    CrossRef  Google Scholar 

  25. Zimmerman, B.J.: From cognitive modeling to self-regulation: a social cognitive career path. Educ. Psychol. 48, 135–147 (2013). https://doi.org/10.1080/00461520.2013.794676

    CrossRef  Google Scholar 

  26. Hu, S., Kuh, G.D., Li, S.: The effects of engagement in inquiry-oriented activities on student learning and personal development. Innov. High. Educ. 33, 71–81 (2008)

    CrossRef  Google Scholar 

  27. Butler, D.L., Schnellert, L.: Collaborative inquiry in teacher professional development. Teach. Teach. Educ. 28, 1206–1220 (2012)

    CrossRef  Google Scholar 

  28. Zafra-Gómez, J., Román-Martínez, I., Gómez-Miranda, M.: Measuring the impact of inquiry-based learning on outcomes and student satisfaction. Assess. Eval. High. Educ. 40(8), 1050–1069 (2015)

    CrossRef  Google Scholar 

  29. Spronken-Smith, R., Angelo, T., Matthews, H., O’Steen, B., Robertson, J.: How Effective is Inquiry-Based Learning in Linking Teaching and Learning. International Policies and Practices for Academic Enquiry. Marwell, Winchester, UK (2007)

    Google Scholar 

  30. Mazur, E.: Peer Instruction: A User’s Manual. Prentice Hall, Upper Saddle River, NJ (1997)

    Google Scholar 

  31. Cecchinato, G., Foschi, L.C.: Perusall: university learning-teaching innovation employing social annotation and machine learning. Qwerty: Open Interdiscipl. J. Technol. Cult. Educ. 15(2) (2020). https://doi.org/10.30557/QW000030

  32. Biro, S.: Reading in a time of crisis: using Perusall to facilitate close reading and active discussion in the remote philosophy classroom. Teach. Philos. (2021)

    Google Scholar 

  33. Adams, B., Wilson, N.S.: Building community in asynchronous online higher education courses through collaborative annotation. J. Educ. Technol. Syst. 49(2) (2020)

    Google Scholar 

  34. Walker, A.S.: Perusall: harnessing AI robo-tools and writing analytics to improve student learning and increase instructor efficiency. J. Writing Analytics 3 (2019)

    Google Scholar 

  35. Suhre, C.J.M., Winnips, J.C., de Boer, V., Valdivia, P., Beldhuis, H.J.A.: Students’ experiences with the use of a social annotation tool to improve learning in flipped classrooms. In: 5th International Conference on Higher Education Advances (HEAd 2019) (2019)

    Google Scholar 

  36. Lee, S.C., Yeong, F.M.: Fostering student engagement using online, collaborative reading assignments mediated by Perusall. Asia-Pacific Scholar 3(3) (2018)

    Google Scholar 

  37. Krippendorff, K.: Content Analysis: An Introduction to its Methodology, 2nd edn. Sage, Thousand Oaks, CA (2004)

    Google Scholar 

  38. Berg, B.L., Lune, H.: Qualitative research methods for the social sciences, 9th ed. Pearson Education Limited (2017). ISBN 10: 129-2-16439-5

    Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratory of Distance Education and eLearning, Universidade Aberta, Lisbon, Portugal

    Manuela Francisco

  2. Center for Studies in Education and Innovation, Politécnico de Leira, Leiria, Portugal

    Manuela Francisco

  3. Universidade Aberta, Lisbon, Portugal

    Cristina Amado

Authors
  1. Manuela Francisco
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  2. Cristina Amado
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Corresponding author

Correspondence to Manuela Francisco .

Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan

    Yueh-Min Huang

  2. National Cheng Kung University, Tainan, Taiwan

    Chin-Feng Lai

  3. University of Trás-os-Montes and Alto Douro, Villa Real, Portugal

    Tânia Rocha

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Francisco, M., Amado, C. (2021). Perusall’s Machine Learning Towards Self-regulated Learning. In: Huang, YM., Lai, CF., Rocha, T. (eds) Innovative Technologies and Learning. ICITL 2021. Lecture Notes in Computer Science(), vol 13117. Springer, Cham. https://doi.org/10.1007/978-3-030-91540-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-91540-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91539-1

  • Online ISBN: 978-3-030-91540-7

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